Using Mixed Methods to Explore Field Sketching: An Example from the Hat Creek Fault Zone

Authors

Sketching is a common yet powerful means of communication and visualization in the geosciences. Particularly in field settings, geoscientists sketch in order to record data, explore interpretations, and communicate with peers. However, little prior work has examined the practice of field sketching, perhaps partly due to the "messy" nature of sketches as empirical data.

Here, we describe how mixed methods were used to explore sketches made by expert geoscientists and non-geoscientists during a field trip to the Hat Creek fault zone (northern California, USA) taken as part of the 2013 AAPG Hedberg Research Conference. A total of 361 sketches of the normal fault system were collected from oil and gas industry geologists and seismic interpreters (n=20), academic geologists (n=16), and non-geoscientist software developers and cognitive scientists (n=6) during stops at three field modules. Sketches were first qualitatively analyzed by thematic coding to capture the range of sketch types (e.g., map, perspective landscape view, cross section, 3D block diagram) and annotations (e.g., fault symbols, reference locations, questions, edits, labels). The volume of sketches necessitated transforming qualitative data to a quantitative scheme in order to summarize results and compare sketching preferences across field trip stops and participant groups. Codes were counted and treated as nominal-level data. Differences in code frequency were tested for significance using the Pearson chi-square test of independence.

Results suggest that sketching preferences appear to be largely driven by characteristics of the field trip stop and/or the particular task required. Few significant differences were found between groups, except that academic geoscientists more frequently drew related sets of 2D sketches whereas industry geoscientists more frequently edited and included text explaining their thinking. We offer this study as an example of how a mixed methods approach can be useful in exploring large yet "messy" datasets.

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